Remote sensing for mapping natural habitats and their conservation status – New opportunities and challenges

Safeguarding the diversity of natural and semi-natural habitats in Europe is one of the aims set out by the Habitats Directive (Council Directive 92/43/EEC on the conservation of natural habitats and of wild fauna and flora) and one of the targets of the European 2020 Biodiversity Strategy, and is to be accomplished by maintaining a favourable conservation status. To reach this aim a high-level understanding of the distribution and conditions of these habitats is needed. Remote sensing can considerably contribute to habitat mapping and their observation over time. Several European projects and a large number of scientific studies have addressed the issue of mapping and monitoring natural habitats via remote sensing and the deriving of indicators on their conservation status. The multitude of utilized remote sensing sensors and applied methods used in these studies, however, impede a common understanding of what is achievable with current state-of-the-art technologies. The aim of this paper is to provide a synthesis on what is currently feasible in terms of detection and monitoring of natural and semi-natural habitats with remote sensing. To focus this endeavour, we concentrate on those studies aimed at direct mapping of individual habitat types or discriminating between different types of habitats occurring in relatively large, spatially contiguous units. By this we uncover the potential of remote sensing to better understand the distribution of habitats and the assessment of their conservation status in Europe.     

Impact of the spatial resolution of climatic data and soil physical properties on regional corn yield predictions using the STICS crop model

The assimilation of Earth observation (EO) data into crop models has proven to be an efficient way to improve yield prediction at a regional scale by estimating key unknown crop management practices. However, the efficiency of prediction depends on the uncertainty associated with the data provided to crop models, particularly climatic data and soil physical properties. In this study, the performance of the STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard) crop model for predicting corn yield after assimilation of leaf area index derived from EO data was evaluated under different scenarios. The scenarios were designed to examine the impact of using fine-resolution soil physical properties, as well as the impact of using climatic data from either one or four weather stations across the region of interest. The results indicate that when only one weather station was used, the average annual yield by producer was predicted well (absolute error <5%), but the spatial variability lacked accuracy (root mean square error = 1.3 t ha⁻¹). The model root mean square error for yield prediction was highly correlated with the distance between the weather stations and the fields, for distances smaller than 10 km, and reached 0.5 t ha⁻¹ for a 5-km distance when fine-resolution soil properties were used. When four weather stations were used, no significant improvement in model performance was observed. This was because of a marginal decrease (30%) in the average distance between fields and weather stations (from 10 to 7 km). However, the yield predictions were improved by approximately 15% with fine-resolution soil properties regardless of the number of weather stations used. The impact of the uncertainty associated with the EO-derived soil textures and the impact of alterations in rainfall distribution were also evaluated. A variation of about 10% in any of the soil physical textures resulted in a change in dry yield of 0.4 t ha⁻¹. Changes in rainfall distribution between two abundant rainfalls during the growing season led to a significant change in yield (0.5 t ha⁻¹ on average). Our results highlight the importance of using fine-resolution gridded daily precipitation data to capture spatial variations of rainfall as well as using fine-resolution soil properties instead of coarse-resolution soil properties from the Canadian soil dataset, especially for regions with high pedodiversity.     

利用Allan方差分析GPS非差随机模型特性

非差GPS定位中,通常采用以观测值服从高斯白噪声条件的估计准则进行参数求解。研究表明卫星端误差、传播路径误差、测站环境误差等会破坏观测值的白噪声特性,并且未模型化误差同样具有不利影响。这不仅破坏了估计准则的假设条件,而且部分非白噪声有可能被状态参数吸收,影响估计的准确性。本文将观测值白噪声、有色噪声和未模型化误差一同纳入GPS非差随机模型,以验后残差来表征GPS数据的随机特性,进行Allan方差分析,研究噪声成分及其参数。结果表明,GPS非差噪声组合主要为WN+GM,相位白噪声为2.392mm,GM过程噪声为4.450mm/s,相关时间为52.074s,伪距白噪声为0.936m,GM过程噪声为0.833m/s,相关时间为14.737s,相位的GM过程噪声与卫星相关性较大,而其余噪声则与测站相关性较大,大量分析结果表明GPS非差随机模型并不服从高斯白噪声假设,有待精化。     

三频BDS电离层延迟改正分析

针对电离层改正会引起观测噪声放大,尤其三频二阶改正致使观测噪声被过分放大的不足,该文分别推导了BDS电离层双频一阶改正、三频一阶改正和三频二阶改正模型及三频载波无电离层组合模型。改正电离层延迟的同时会不同程度地放大观测噪声,为此分析比较了各种方法改正后的观测值精度和观测噪声,并对不同方法的应用进行了分析。     

北斗三频数据线性组合优化分析

随着GPS现代化的实施和欧洲Galileo计划的实行,多频数据组合逐渐成为卫星导航定位的发展方向。针对关于北斗导航定位系统多频数据组合的研究较少这一现状,该文首先简要介绍了北斗导航定位系统,并在分析BDS三频数据线性组合的数学模型和确保模糊度参数整数特性的基础之上,给出了BDS三频数据线性组合的优化选取标准;然后从消除或减弱电离层延迟、对流层延迟和观测噪声三个方面系统地分析了BDS三频数据线性组合系数的优化选取问题;最后通过MATLAB模拟,给出了一些典型的组合,分析了它们可能的应用。     

地磁秒采样数据智能分析系统软件

地磁秒采样仪器观测过程中,易受各种环境干扰造成数据异常,如磁暴、高压直流输电、人为干扰、仪器故障等,不易被发现并识别,若不能及时处理,将会造成观测数据质量下降。若诸多干扰与磁扰叠加,将更不易识别,导致数据的错误处理。为了提高观测质量,利用现有编程技术,开发地磁秒采样数据智能分析系统软件,实现对地磁观测仪器工作状态、各种干扰、磁暴等现象的实时监控和分析,及时发现异常并报警,同时利用多台数据对比智能分析软件,对复杂干扰和高压直流输电干扰进行自动识别,并数据曲线中标示干扰位置,提高数据资料处理的准确性和有效性。     

雨量计加热装置研制

雨量计是地震监测中的辅助仪器,用于测量大气降水量。北方冬季严寒,固态降水不能被及时有效记录,且在融化过程中可能会凝结在雨量计传感器中,造成传感器不能记录甚至被冻坏。为此设计一种加热装置,通过加装智能识别、桶内加热器、延时等装置,对现有非融雪式雨量计进行改造,将固态降水有效转化为液态降水,从而得到降水量的真实记录。     

鲁14井数字化水位潮汐响应特性初析

以井—含水层系统潮汐理论为基础,利用Baytap-G潮汐分析程序计算鲁14井数字化水位的潮汐响应特征参数,提取井水位潮汐因子和相位差,分析远场大震前后井水位潮汐响应特征参数的变化情况,同时对固体潮和气压影响量进行分离,以期深入了解井水位微动态变化特征,为该井水位观测资料的动态评价和效能评估提供参考。结果表明：鲁14井潮汐地下水流类型以径向流为主,潮汐因子和相位差变化相对稳定,但受远场大震影响显著,其含水层渗透性随潮汐参数的增大（减小）而增大（减小）;地震波作用可使含水层渗透性发生变化,但并非导致含水层渗透性变化的主要因素。     

山东数字测震台站台基背景噪声分析

介绍了山东数字地震台网基本情况,计算了40个测震台站台基背景噪声,利用Welch方法计算噪声功率谱密度（PSD）,进而计算地震台台基1—20 Hz地动噪声均方根值（RMS）和有效动态观测范围。根据计算结果,依照《地震台站观测环境技术要求》,对山东测震数字台网40个参评测震台站进行背景噪声级别分类,并分析不同台站背景噪声水平较低的原因,以期为测震台网的优化建设提供数据支持。     

运输工具对相对重力仪测量精度的影响分析

简要介绍了当前常用重力仪类型和标称精度,并就运输工具性能好坏、重力测段大小、路线距离长短和观测时间长短对相对重力仪的观测精度的影响进行测定分析,结果表明:影响重力仪观测精度的主要因素与汽车运输工具有关,选择越野性能好,减震效果好的汽车作为完成高等级流动重力测量任务的前提条件。